Purpose: Automatically terminate conversations when appropriate conditions are met.

Trigger conditions: The LLM should call this tool when:

• The main task has been completed and user is satisfied
• The conversation reached natural conclusion with mutual agreement
• The user explicitly indicates they want to end the conversation

Parameters:

• reason (string, required): The reason for ending the call
• message (string, optional): A farewell message to send to the user before ending the call

Function call format:

1
{
2
  "type": "function",
3
  "function": {
4
    "name": "end_call",
5
    "arguments": "{\"reason\": \"Task completed successfully\", \"message\": \"Thank you for using our service. Have a great day!\"}"
6
  }
7
}

Implementation: Configure as a system tool in your agent settings. The LLM will receive detailed instructions about when to call this function.

Learn more: End call tool

Purpose: Automatically switch to the user’s detected language during conversations.

Trigger conditions: The LLM should call this tool when:

• User speaks in a different language than the current conversation language
• User explicitly requests to switch languages
• Multi-language support is needed for the conversation

Parameters:

• reason (string, required): The reason for the language switch
• language (string, required): The language code to switch to (must be in supported languages list)

Function call format:

1
{
2
  "type": "function",
3
  "function": {
4
    "name": "language_detection",
5
    "arguments": "{\"reason\": \"User requested Spanish\", \"language\": \"es\"}"
6
  }
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}

Implementation: Configure supported languages in agent settings and add the language detection system tool. The agent will automatically switch voice and responses to match detected languages.

Learn more: Language detection tool

Purpose: Transfer conversations between specialized AI agents based on user needs.

Trigger conditions: The LLM should call this tool when:

• User request requires specialized knowledge or different agent capabilities
• Current agent cannot adequately handle the query
• Conversation flow indicates need for different agent type

Parameters:

• reason (string, optional): The reason for the agent transfer
• agent_number (integer, required): Zero-indexed number of the agent to transfer to (based on configured transfer rules)

Function call format:

1
{
2
  "type": "function",
3
  "function": {
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    "name": "transfer_to_agent",
5
    "arguments": "{\"reason\": \"User needs billing support\", \"agent_number\": 0}"
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  }
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}

Implementation: Define transfer rules mapping conditions to specific agent IDs. Configure which agents the current agent can transfer to. Agents are referenced by zero-indexed numbers in the transfer configuration.

Learn more: Agent transfer tool

Purpose: Seamlessly hand off conversations to human operators when AI assistance is insufficient.

Trigger conditions: The LLM should call this tool when:

• Complex issues requiring human judgment
• User explicitly requests human assistance
• AI reaches limits of capability for the specific request
• Escalation protocols are triggered

Parameters:

• reason (string, optional): The reason for the transfer
• transfer_number (string, required): The phone number to transfer to (must match configured numbers)
• client_message (string, required): Message read to the client while waiting for transfer
• agent_message (string, required): Message for the human operator receiving the call

Function call format:

1
{
2
  "type": "function",
3
  "function": {
4
    "name": "transfer_to_number",
5
    "arguments": "{\"reason\": \"Complex billing issue\", \"transfer_number\": \"+15551234567\", \"client_message\": \"I'm transferring you to a billing specialist who can help with your account.\", \"agent_message\": \"Customer has a complex billing dispute about order #12345 from last month.\"}"
6
  }
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}

Implementation: Configure transfer phone numbers and conditions. Define messages for both customer and receiving human operator. Works with both Twilio and SIP trunking.

Learn more: Transfer to human tool

Purpose: Allow the agent to pause and wait for user input without speaking.

Trigger conditions: The LLM should call this tool when:

• User indicates they need a moment (“Give me a second”, “Let me think”)
• User requests pause in conversation flow
• Agent detects user needs time to process information

Parameters:

• reason (string, optional): Free-form reason explaining why the pause is needed

Function call format:

1
{
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  "type": "function",
3
  "function": {
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    "name": "skip_turn",
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    "arguments": "{\"reason\": \"User requested time to think\"}"
6
  }
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}

Implementation: No additional configuration needed. The tool simply signals the agent to remain silent until the user speaks again.

Learn more: Skip turn tool

You may pass additional parameters to your custom LLM implementation.

1
from elevenlabs.conversational_ai.conversation import Conversation, ConversationConfig
2
3
extra_body_for_convai = {
4
    "UUID": "123e4567-e89b-12d3-a456-426614174000",
5
    "parameter-1": "value-1",
6
    "parameter-2": "value-2",
7
}
8
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config = ConversationConfig(
10
    extra_body=extra_body_for_convai,
11
)

1
import json
2
import os
3
import fastapi
4
from fastapi.responses import StreamingResponse
5
from fastapi import Request
6
from openai import AsyncOpenAI
7
import uvicorn
8
import logging
9
from dotenv import load_dotenv
10
from pydantic import BaseModel
11
from typing import List, Optional
12
13
# Load environment variables from .env file
14
load_dotenv()
15
16
# Retrieve API key from environment
17
OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
18
if not OPENAI_API_KEY:
19
    raise ValueError("OPENAI_API_KEY not found in environment variables")
20
21
app = fastapi.FastAPI()
22
oai_client = AsyncOpenAI(api_key=OPENAI_API_KEY)
23
24
class Message(BaseModel):
25
    role: str
26
    content: str
27
28
class ChatCompletionRequest(BaseModel):
29
    messages: List[Message]
30
    model: str
31
    temperature: Optional[float] = 0.7
32
    max_tokens: Optional[int] = None
33
    stream: Optional[bool] = False
34
    user_id: Optional[str] = None
35
    elevenlabs_extra_body: Optional[dict] = None
36
37
@app.post("/v1/chat/completions")
38
async def create_chat_completion(request: ChatCompletionRequest) -> StreamingResponse:
39
    oai_request = request.dict(exclude_none=True)
40
    print(oai_request)
41
    if "user_id" in oai_request:
42
        oai_request["user"] = oai_request.pop("user_id")
43
44
    if "elevenlabs_extra_body" in oai_request:
45
        oai_request.pop("elevenlabs_extra_body")
46
47
    chat_completion_coroutine = await oai_client.chat.completions.create(**oai_request)
48
49
    async def event_stream():
50
        try:
51
            async for chunk in chat_completion_coroutine:
52
                chunk_dict = chunk.model_dump()
53
                yield f"data: {json.dumps(chunk_dict)}\n\n"
54
            yield "data: [DONE]\n\n"
55
        except Exception as e:
56
            logging.error("An error occurred: %s", str(e))
57
            yield f"data: {json.dumps({'error': 'Internal error occurred!'})}\n\n"
58
59
    return StreamingResponse(event_stream(), media_type="text/event-stream")
60
61
if __name__ == "__main__":
62
    uvicorn.run(app, host="0.0.0.0", port=8013)

1
{
2
  "messages": [
3
    {
4
      "role": "system",
5
      "content": "\n  <Redacted>"
6
    },
7
    {
8
      "role": "assistant",
9
      "content": "Hey I'm currently unavailable."
10
    },
11
    {
12
      "role": "user",
13
      "content": "Hey, who are you?"
14
    }
15
  ],
16
  "model": "gpt-4o",
17
  "temperature": 0.5,
18
  "max_tokens": 5000,
19
  "stream": true,
20
  "elevenlabs_extra_body": {
21
    "UUID": "123e4567-e89b-12d3-a456-426614174000",
22
    "parameter-1": "value-1",
23
    "parameter-2": "value-2"
24
  }
25
}